mlsanityData to 14 Oct 2026

Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests

arXiv 2505.10033 ↗First posted 15 May 2025cs.RO cs.LGcited by 2

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Despite significant advancements in Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), their robustness in real-world conditions, particularly under external disturbances, remains insufficiently explored. In this paper, we evaluate the resilience of a DRL-based agent designed to capture floating waste under various perturbations. We train the agent using domain randomization and evaluate its performance in real-world field tests, assessing its ability to handle unexpected disturbances such as asymmetric drag and an off-center payload. We assess the agent's performance under these perturbations in both simulation and real-world experiments, quantifying performance degradation and benchmarking it against an MPC baseline. Results indicate that the DRL agent performs reliably despite significant disturbances. Along with the open-source release of our implementation, we provide insights into effective training strategies, real-world challenges, and practical considerations for deploying DRLbased ASV controllers.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 29 references that are in the map. First posted on arXiv 15 May 2025.

Builds on

10 of 29 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation11 Jul

Other references · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  3. 2018PAMPC: Perception-Aware Model Predictive Control for Quadrotorscited by 28
  4. 2020Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehiclescited by 11
  5. 2022Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environmentcited by 11
  6. 2023Robust Unmanned Surface Vehicle Navigation with Distributional Reinforcement Learningcited by 8
  7. 2023Deep Reinforcement Learning Based Tracking Control of an Autonomous Surface Vessel in Natural Waterscited by 6
  8. 2023RANS: Highly-Parallelised Simulator for Reinforcement Learning based Autonomous Navigating Spacecraftscited by 6
  9. 2024OtterROS: Picking and Programming an Uncrewed Surface Vessel for Experimental Field Robotics Research with ROS 2cited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capturecited by 0
  2. 2026Sim-to-Real RL for ASVs using SysIDcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Review of Deep Reinforcement Learning for Autonomous Driving
  2. 2024Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
  3. 2025Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems
  4. 2024Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping
  5. 2024Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
  6. 2025Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
  7. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
  8. 2025The Emergence of Deep Reinforcement Learning for Path Planning